System and Method of Streaming 3-D Wireframe Animations

ABSTRACT

Optimal resilience to errors in packetized streaming 3-D wireframe animation is achieved by partitioning the stream into layers and applying unequal error correction coding to each layer independently to maintain the same overall bitrate. The unequal error protection scheme for each of the layers combined with error concealment at the receiver achieves graceful degradation of streamed animation at higher packet loss rates than approaches that do not account for subjective parameters such as visual smoothness.

PRIORITY CLAIM

The present application is a continuation of U.S. patent applicationSer. No. 13/863,679, filed Apr. 16, 2013, which is a continuation ofU.S. patent application Ser. No. 11/059,118, filed Feb. 16, 2005, nowU.S. Pat. No. 8,421,804, issued Apr. 16, 2013, which is a continuationof PCT/US 03/25761 filed on Aug. 15, 2003, which claims priority to U.S.Provisional Patent Application No. 60/404,410, filed Aug. 20, 2002. Thecontents of these applications are incorporated herein by reference intheir entirety.

RELATED APPLICATION

The present application is related to Non-Provisional application Ser.No. 10/198,129, filed Jul. 19, 2002, assigned to the same assignee asthat of the present application and fully incorporated herein byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to streaming data and more specificallyrelates to a system and method of streaming 3-D wireframe animations.

2. Introduction

The Internet has rapidly evolved during the past few years from alow-bandwidth, text-only collaboration medium, to a rich, interactive,real-time, audio-visual virtual world. It involves many users,environments and applications, where 3-D animations constitute a drivingforce. Animated 3-D models enable intuitive and realistic interactionwith displayed objects and allow for effects that cannot be achievedwith conventional audio-visual animations. Consequently, the currentchallenge is to integrate animated 3-D geometry as a new data stream inthe existing evolving infrastructure of the Internet, in a way that bothenhances the existing networked environment and respects its limitedresources. Although static 3-D mesh geometry compression has beenactively researched in the past decade, very little research has beenconducted in compressing dynamic 3-D geometry, which is an extension ofstatic 3-D meshes to the temporal domain.

The most prevalent representations for 3-D static models are polygonalor triangle meshes. These representations allow for approximate modelsof arbitrary shape and topology within some desired precision orquality. Efficient algorithms and data structures exist to generate,modify, compress, transmit and store such static meshes. Future,non-static, stream types that introduce the time dimension, wouldrequire scalable solutions to survive with respect to the network'slimited resources (bandwidth) and characteristics (channel errors).

The problem of 3-D wireframe animation streaming addressed herein can bestated as follows: Assume (i) a time-dependent 3-D mesh has beenscalably compressed in a sequence of wireframe animation frames, (ii)the available transmission rate R is known (or determined with respectto the corresponding TCP-friendly rate), (iii) the channel errorcharacteristics are known, and (iv) a fraction C of the availabletransmission rate (C<R) can be reserved for channel coding. Then, theissue is to identify the optimal number of bits to be allocated to eachlevel of importance (layer) in the animation scene that maximizes theperceived quality of the time-dependent mesh at the receiver.

Most animation coding approaches use objective metrics to achieve ahierarchical coding of static 3-D meshes. What is needed is an animationapproach that utilizes a subjective quantity, such as visual smoothness,to provide an improved appearance of animation. Described herein is a3-D wireframe animation codec and its bitstream content, along with theassociated forward error correction (FEC) codes. The visual distortionmetric as well as the unequal error protection (UEP) method andreceiver-based concealment method are further explained.

SUMMARY OF THE INVENTION

The present invention focuses on source and channel coding techniquesfor error resilient time-dependent 3-D mesh streaming over the Internetthat respects network bandwidth and considers the bursty loss nature ofthe channel.

An exemplary embodiment of the invention is a method of streaming datacomprising computing a visual smoothness value for each node in awireframe mesh and layering data associated with the wireframe mesh intoa plurality of layers such that an average visual smoothness valueassociated with each layer reflects the respective layer's importance inan animation sequence. Other embodiments of the invention may include abitstream generated according to a process similar to the above methodand an apparatus of generating and transmitting a bitstream or receivinga bitstream.

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. Thefeatures and advantages of the invention may be realized and obtained bymeans of the instruments and combinations particularly pointed out inthe appended claims. These and other features of the present inventionwill become more fully apparent from the following description andappended claims, or may be learned by the practice of the invention asset forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered to be limiting of its scope, the invention will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings in which:

FIG. 1A is a block diagram of a 3-D Animation codec;

FIG. 1B is a block diagram of a decoder;

FIG. 2 is a comparative plot of distortion metrics including PSNR,Hausdorff Distance and Visual Smoothness;

FIG. 3A represents a flowchart of method of error resilient wireframestreaming;

FIG. 3B illustrates a flowchart according to an aspect of the invention;

FIG. 4 is a comparative plot of three error concealment methods forsequence for wireframe animation TELLY;

FIG. 5A is a comparative plot of Visual smoothness (VS) transmitted anddecoded frames of 3 layers of the wireframe animation TELLY;

FIG. 5B is another comparative plot of VS; and

FIG. 6 is a comparative plot of Visual Smoothness between transmittedand decoded frames of 2 layers of wireframe animation BOUNCEBALL.

DETAILED DESCRIPTION OF THE INVENTION

Much research has been undertaken to study streaming video acrosscomputer networks in general and over the Internet in particular.Relatively little has been undertaken in the field of streaming 3-Dwireframe animation. Although both processes may have some similarities,the two are significantly different. Different data passes across thenetwork, so loss affects signal reconstruction differently. Theperceptual effects of such loss have been poorly addressed in the art.Much of the work in this area relied on objective measures such as PSNRin lieu of those that take subject effects into account.

The present invention brings together concepts from a number of fieldsto address the problem of how to achieve optimal resilience to errors interms of the perceptual effect at the receiver. In this regard, theinvention relates to a subjective quality of an animation, for example,the mesh surface smoothness. To achieve an improved coding scheme takingsubjective factors into account, an aspect of the invention comprisespartitioning the animation stream into a number of layers and applyingReed-Solomon (RS) forward error correction (FEC) codes to each layerindependently and in such a way as to maintain the same overall bitratewhilst minimizing the perceptual effects of error, as measured by adistortion metric related to static 3-D mesh compression. Gracefuldegradation of streamed animations at higher packet loss rates thanother approaches can be achieved by the unequal error protection (UEP)approach combined with error concealment (EC) and an efficientpacketization scheme.

The present disclosure first provides an overview of the 3D-Animationcodec that introduces the related notation, followed by an overview ofthe error correcting RS codes follows together with derivation of thechannel model, as well as an exemplary description of UEP packetizationfor the encoded bitstream.

The vertices m_(j) of a time-dependent 3-D mesh form the indexed setM_(t)={m_(jt); j=1, 2, . . . , n}, at time t, where n is the number ofvertices in the mesh. Since a vertex has three space components (x_(j),y_(j), z_(j)), and assuming that no connectivity changes occur in time(constant n), we can represent the indexed set's data at time t by theposition matrix M_(t), as:

$M_{t} = \left\lfloor \begin{matrix}x_{1,t} & x_{2,t} & \ldots & x_{n,t} \\y_{1,t} & y_{2,t} & \ldots & y_{n,t} \\z_{1,t} & z_{2,t} & \ldots & z_{n,t}\end{matrix} \right\rfloor$

The indexed set of vertices are partitioned into intuitively naturalpartitions, called nodes. The term “node” is used to highlight thecorrespondence of such nodes with the nodes as defined in the virtualreality markings language (VRML). The position matrix corresponding tothe i^(th) node is denoted by N_(i,t). Note that without loss ofgenerality, the vertex matrix can now be expressed as:

M _(t) =[N _(1,t) N _(2,t) . . . N _(k,t)]

for k such nodes. For notational convenience, the terms N_(i,t) (i=1, 2,. . . , k) are used both for representing a matrix and to refer to thei^(th) node as well. The objective of the 3D-Animation compressionalgorithm is to compress the sequence of matrices M_(t) that form thesynthetic animation, for transmission over a communications channel.Obviously, for free-form animations of a 3-D mesh the coordinates of themesh may exhibit high variance, which makes the M_(t) matricesunsuitable for compression. Hence, the signal can be defined as the setof non-zero displacements of all vertices in all nodes at time t:

D _(t) ={d _(a) =m _(a) −m _(n0) ,i=1,2, . . . ,p(p≦n):d _(a)≠0}

Following this notation, the above can be expressed with a displacementmatrix, D_(t), as:

$D_{t} = {\left. {M_{t} - M_{0}}\Leftrightarrow D_{t} \right. = \begin{bmatrix}{x_{1,t} - x_{1,0}} & {x_{2,t} - x_{2,0}} & \ldots & {x_{p,t} - x_{p,0}} \\{y_{1,t} - y_{1,0}} & {y_{2,t} - y_{2,0}} & \ldots & {y_{p,t} - y_{p,0}} \\{z_{1,t} - z_{1,0}} & {z_{2,t} - z_{2,0}} & \ldots & {z_{p,t} - z_{p,0}}\end{bmatrix}}$

or equivalently, using node matrices:

D _(t) =[F _(1,t) F _(2,t) . . . F _(l,t)]  (1)

where F_(i,t) the displacement matrix of node i, for 1 such nodes (i=1,2, . . . , l). Note that D_(t)'s dimension is reduced to p≦n compared toM_(t), since D_(t) does not contain vertices for which the displacementon all axes is zero. Note, too, that 1≦k holds, in the event that novertices in a node get displaced (F_(i;t)=0). In 3D-Animationterminology, sparse animations are referred to as those sequences withp<n and l<k, whereas if p=n and l=k the animation is called dense. It isevident that if an encoder is capable of controlling parameters p and l,it can generate a layered bitstream (by adjusting parameter l), whereevery layer L can be scalable (by adjusting parameter p). The sparsity(or density) of the animation is qualified by the density factor,defined as:

$\begin{matrix}{{{df} = {\frac{1}{F}\frac{1}{k}{\sum\limits_{f = 1}^{F}{\sum\limits_{j = 1}^{l}\frac{p_{jf}}{n_{jf}}}}}},{{{with}\mspace{14mu} l} \leq {k\mspace{14mu} {and}\mspace{14mu} p} \leq {n.}}} & (2)\end{matrix}$

in the range [0 . . . 1], where F is the number of animation frames andk the number of nodes in the reference model. For p→n and l→k, thendf→1, therefore a complete animation.

The concept described and summarized in equation (1) above, is suited toa DPCM coder, as detailed below. The coding process assumes that theinitial wireframe model M₀, here termed as the reference model, isalready present at the receiver. The reference model can be compressedand streamed with an existing method for static 3-D mesh transmission,along with error protection if it is assumed the transmission is doneover the same lossy channel as the time-dependent mesh. Such existingmethods can accommodate and interoperate with static mesh transmissionsand will not be discussed further here.

In the 3D-Animation codec's context, an I-frame describes changes fromthe reference model M₀ to the model at the current time instant t. AP-frame describes the changes of a model from the previous time instantt−1 to the current time instant t. The corresponding position anddisplacement matrices for I and P frames are denoted respectively byM_(t) ^(I), M_(t) ^(P), D_(t) ^(I), D_(t) ^(P).

FIG. 1A shows an exemplary block diagram 100 of a coding processaccording to an aspect of the invention. The diagram illustrates a DPCMencoder that takes advantage of the temporal correlation of thedisplacement of each vertex along every axis in the 3-D space. To encodea P-frame, the decoded set (animation frame or displacement matrix) ofthe previous instance is used as the predicted value 108, 106{circumflex over (D)}_(t-1) ^(P). (Equivalently for encoding an I-framethe predicted matrix is {circumflex over (D)}_(t-1) ^(I) where at t=0 isthe displacement matrix for the reference model.) Then, the predictionerror E_(t), i.e. the difference between the current displacement matrixand the predicted one 106 is computed 102 and quantized 104 (Ê_(t)).Finally, the quantized samples are entropy coded (CO using an adaptivearithmetic coding algorithm 110 to handle the unknown data statistics.This predictive scheme prevents quantization error accumulation.

A DPCM decoder 120 is shown in FIG. 1B. The decoder 120 first decodesarithmetically the received samples 122 (C′_(t)) and computes thedecoded samples 124, 126 ({circumflex over (D)}_(t)′). The quantizationrange of each node is determined by their bounding box. The quantizationstep size can be assumed to be the same for all nodes, or can vary inorder to shape the encoded bitstream rate. Allowing differentquantization step sizes for different nodes may result in artifacts suchas mesh cracks, especially in the boundaries between nodes.

The discloser mentions above that D_(t)'s dimension is reduced to p≦ncompared to M_(t), since it does not contain vertices for which thedisplacement on all axes is zero. This property provides an advantageagainst MPEG-4's BIFS-Animation, which does not allow for reducedanimation frames. For sparse D_(t) matrices it may also be the case thata whole node is not animated thus allowing great animation flexibilityand generating a scalable bitstream. Furthermore, in the case whereF_(i,t)=0, ∀iε[1 . . . l], the displacement matrix D_(t) is zero,leading to an ‘empty’ frame. This property resembles the silence periodinherent in speech audio streams and can be exploited in the applicationlayer of RTP-based receivers to absorb network jitter. Inter-streamsynchronization can also be achieved, which is paramount for manyapplications (e.g. lip synchronization of a 3-D animated virtualsalesman with packet speech).

Next is described a channel model and error correction codes. The ideaof Forward Error Correction (FEC) is to transmit additional redundantpackets which can be used at the receiver to reconstruct lost packets.In the FEC process according to a preferred embodiment of the presentinvention, Reed-Solomon (RS) codes are used across packets. RS codes arethe only non-trivial maximum distance separable codes known, hence theyare suitable for protection against packet losses over bursty losschannels. An RS(n, k) code of length n and dimension k is defined overthe Galois Field GF (2^(q)) and encodes k q-bit information symbols intoa codeword of n such symbols, i.e. n≦2^(q)−1. A sender needs to storecopies of k information packets in order to calculate n−k redundancypackets. The resulting n packets are stacked in a block of packet (BOP)structure. This BOP structure is known in the art and thus not explainedfurther herein. To maintain a constant total channel data rate, thesource rate is reduced by the fraction k/n, called the code rate,resulting in an initially reduced animation quality. A receiver canbegin decoding as soon as it receives any k correct symbols, or packetsof a BOP.

In reality, the underlying bursty loss process of the Internet is quitecomplex, but it can be closely approximated by a 2-state Markov model.The two states are state G (good), where packets are timely andcorrectly received, and B (bad), where packets are either lost ordelayed to the point that they that can be considered lost. The statetransition probabilities p_(GB) and p_(BG) fully describe the model, butsince they are not sufficiently intuitive, the model can be expressedusing the average loss probability P_(B), and the average burst lengthL_(B), as:

$\begin{matrix}{P_{B} = {{\Pr (B)} = \frac{p_{GB}}{p_{GB} + p_{BG}}}} & (3) \\{L_{B} = {1/{p_{BG}.}}} & (4)\end{matrix}$

For the selection of the RS code parameters the probability needs to beknown that a BOP cannot be reconstructed by the erasure decoder as afunction of the channel and the RS code parameters. For an RS(n, k)code, this is the probability that more than n−k packets are lost withina BOP, and it is called the block error rate, P_(BER). Let P (m, n) bethe probability of m lost packets within a block of n packets, alsocalled the block error density function. Then, the calculation is:

$\begin{matrix}{P_{BER} = {\sum\limits_{m = {n - k + 1}}^{n}{P\left( {m,n} \right)}}} & (5)\end{matrix}$

The average loss probability P_(B) and the average loss burst L_(B)corresponding to the 2-state Markov model described above, relate toblock error density function P (m, n). The exact nature of theirrelationship has been extensively studied and derived in the literature.Here we adapt the derivation for bit error channels to a packet losschannel.

The Markov model as described before is a renewal model, i.e. a lossevent resets the loss process. Such a model is determined by thedistribution of error-free intervals (gaps). If there occurs an event ofgap length v such that v−1 packets are received between two lostpackets, then the gap density function g(v) gives the probability of agap length v, i.e. g(v)=Pr(0^(v-1)|1). The gap distribution functionG(v) gives the probability of a gap length greater than v−1, i.e.G(v)=Pr(0^(v-1)|1). In state B of our model all packets are lost, whilein state G all packets are received, yielding:

${g(v)} = \left\{ {{\begin{matrix}{{1 - p_{GB}},} & {v = 1} \\{{{p_{BG}\left( {1 - p_{GB}} \right)}^{v - 2}p_{GB}},} & {v > 1}\end{matrix}{G(v)}} = \left\{ \begin{matrix}{1,} & {v = 1} \\{{p_{BG}\left( {1 - p_{GB}} \right)}^{v - 2},} & {v > 1}\end{matrix} \right.} \right.$

Let R(m, n) be the probability of m−1 packet losses within the next n−1packets following a lost packet. This probability can be calculated fromthe recurrence:

${R\left( {m,n} \right)} = \left\{ \begin{matrix}{{G(n)},} & {m = 1} \\{{\sum\limits_{v = 1}^{n - m + 1}{{g(v)}{R\left( {{m - 1},{n - v}} \right)}}},} & {2 \leq m \leq n}\end{matrix} \right.$

Then, the block error density function P (m, n) or probability of m lostpackets within a block of n packets is given by:

${P\left( {m,n} \right)} = \left\{ \begin{matrix}{{1 - {\sum\limits_{v = 1}^{n}{P\left( {m,v} \right)}}},} & {m = 0} \\{{\sum\limits_{v = 1}^{n - m + 1}{P_{B}{G(v)}{R\left( {m,{n - v + 1}} \right)}}},} & {1 \leq m \leq n}\end{matrix} \right.$

where P_(B) is the average error probability.

From Eq. 5, it is noted that P (m, n) determines the performance of theFEC scheme, and can be expressed as a function of P_(B), L_(B) using Eq.3 and 4. As explained below, the expression of P (m, n) can be used in aRS(m, n) FEC scheme for optimized source/channel rate allocation thatminimizes the visual distortion.

Next is described a bitstream format and packetization process. Theoutput bitstream of the 3D-Animation codec needs to be appropriatelypacketized for streaming with an application-level transport protocol,e.g. RTP. This process for a single layer bit-stream is known and itsmain features are summarized by the following three concepts:

(1) In order to describe which nodes of the model are to be animated theanimation masks, NodeMask and VertexMasks are defined in a similar wayto BIFS-Anim. The NodeMask is essentially a bit-mask where each bit, ifset, denotes that the corresponding node in the Node Table will beanimated. The Node Table (an ordered list of all nodes in the scene) iseither known a priori at the receiver since the reference wireframemodel exists there already, or is downloaded by other means. In asimilar way, the VertexMasks are defined, one per axis, for the verticesto be animated.

(2) In its simplest form, one frame (which represents one ApplicationData Unit (ADU)), is contained in one RTP packet. In this sense, the3D-Animation codec's output bit-stream is ‘naturally packetizable’according to the known Application Level Framing (ALF) principle. An RTPpacket payload format is considered starting with the NodeMask andVertexMasks, followed by the encoded samples along each axis.

(3) The M bit in the RTP header must be set for the first of a series of‘empty’ frames, which (if they exist) can be grouped together.

This simple format suffices for light animations with a modest number ofvertices. However, sequences with high scene complexity orhigh-resolution meshes may generate a large number of coded data aftercompression, resulting in frames that potentially exceed the path MTU.In such cases, raw packetization in a single layer would require thedefinition of fragmentation rules for the RTP payload, which may notalways be straightforward in the ALF sense. Furthermore, frames directlypacketized in RTP as described above generate a variable bitrate streamdue to their varying lengths.

A more efficient packetization scheme is sought that satisfies therequirements set out above: (a) to accommodate layered bitstreams, and(b) to produce a constant bitrate stream. This efficiency can beachieved by appropriately adapting the block structure known asBlock-Of-Packets (BOP). In this method, encoded frames of a single layerare placed sequentially in line order of an n-line by S_(P)-column gridstructure and then RS codes are generated vertically across the grid.For data frames protected by an RS (n, k) erasure code, error resilienceinformation is appended so that the length of the grid is n for k framesof source data. This method is most appropriate for packet networks withburst packet errors, and can be fully described by the sequence framerate FR, the packet size Sp, the data frame rate in a BOP F_(BOP), andthe RS code (n, k).

Intuitively, for a BOP consisting of F_(BOP) data frames, with Sp byteslong packets, at FR frame rate, the total source and channel bitrate Ris given by:

$\begin{matrix}{R = \frac{n \cdot {FR} \cdot S_{P}}{F_{BOP}}} & (6)\end{matrix}$

This equation serves as a guide to the design of efficient packetizationschemes by appropriately balancing the parameters F_(BOP), n and S_(P).It also encompasses the trade-off between delay and resilience. For alayered bitstream, a design is needed for one BOP structure per layer.By varying the parameters in Eq. 6, different RS code rates can beallocated to each layer, thus providing unequal level of errorprotection to each layer. The way these parameters are adjusted inpractice for the application of 3-D animation streaming, considering ameasure of visual error, is explained next.

In order to measure the visual loss resulting from a non-perfectreconstruction of the animated mesh at the receiver, a metric isrequired that is able to capture the visual difference between theoriginal mesh M_(t) at time t and its decoded equivalent {circumflexover (M)}_(t). The simplest measure is the RMS geometric distancebetween corresponding vertices. Alternatively, the Hausdorff Distancehas been commonly used as an error metric. The Hausdorff distance isdefined in the present case as the maximum minimum distance between thevertices of two sets, M_(t) and {circumflex over (M)}_(t) in such a waythat every point M_(t) lies within the distance H (M_(t), {circumflexover (M)}_(t)) of every point in {circumflex over (M)}_(t) and viceversa. This can be expressed as:

$\begin{matrix}{{{H\left( {M_{t},{\hat{M}}_{t}} \right)} = {\max\left( {{h\left( {M_{t},{\hat{M}}_{t}} \right)},{h\left( {{\hat{M}}_{t},M_{t}} \right)}} \right)}}{{{{where}\mspace{14mu} {h\left( {M_{t},{\hat{M}}_{t}} \right)}} = {\max\limits_{m_{t} \in M_{t}}{\min\limits_{\hat{m_{t}} \in \hat{M_{t}}}{{m_{t} - {\hat{m}}_{t}}}}}},}} & (7)\end{matrix}$

and ∥•∥ is the Euclidean distance between the two vertices, m_(t) and{circumflex over (m)}_(t). Many other distortion metrics can be derivedby equivalence to natural video coding, such SNR and PSNR, but they aretailored to the statistical properties of the specific signal theyencode, failing to give a uniform measure of user perceived distortionacross a number of signals and encoding methods over different media.Moreover, especially for 3-D meshes, all these metrics give onlyobjective indications of geometric closeness, or signal to noise ratios,and they fail to capture the more subtle visual properties the human eyeappreciates, such as surface smoothness.

FIG. 2 illustrates a comparative plot 200 of distortion metrics: PSNR,Hausdorff Distance, and Visual Smoothness for 150 frames of the animatedsequence BOUNCEBALL with I-frame frequency at 8 Hz. The two upper plots(PSNR-Hausdorff) show the expected correlation between the correspondingmetrics of geometric distance and Hausdorff Distance (eq. 7) theyrepresent. The two lower plots indicate that the visual distortion (eq.8) might be low in cases where the geometric distance is high andvice-versa.

One attempt that was made in the direction of using surface smoothnesswas reported by Karni and Gotsman as being undertaken whilst evaluatingtheir spectral compression algorithm for 3-D mesh geometries. See, ZachiKarni and Craig Gotsman, “Spectral compression for mesh geometry,” inSiggraph 2000, Computer Graphics Proceedings, Kurt Akeley, Ed. 2000, pp.279-286, ACM Press/ACM SIGGRAPH/Addison Wesley Longman, incorporatedherein by reference. In this, the suggested 3-D mesh distortion metricnormalizes the objective error computed as the Euclidean Distancebetween two vertices, by each vertex's distance to its adjacentvertices. This type of error metric captures the surface smoothness ofthe 3-D mesh. This may be achieved by a Laplacian operator, which takesinto account both topology and geometry. The value of this geometricLaplacian at vertex v_(i) is:

${{GL}\left( v_{i} \right)} = {v_{i} - \frac{\sum\limits_{j \in {n{(i)}}}{l_{ij}^{- 1}v_{j}}}{\sum\limits_{j \in {n{(i)}}}l_{ij}^{- 1}}}$

where n(i) is the set of indices of the neighbors of vertex i, andl_(ij) is the geometric distance between vertices i and j. Hence, thenew metric is defined as the average of the norm of the geometricdistance between meshes and the norm of the Laplacian difference (m_(t){circumflex over (m)}_(t) are the vertex sets of meshesM_(t),{circumflex over (M)}_(t) respectively, and n the set size ofM_(t),{circumflex over (M)}_(t)):

$\begin{matrix}{{{M_{t} - \hat{M_{t}}}} = {\frac{1}{2n}\left( {{{m_{t} - \hat{m_{t}}}} + {{{{GL}\left( m_{t} \right)} - {{GL}\left( \hat{m_{t}} \right)}}}} \right)}} & (8)\end{matrix}$

This metric in Eq. 8 is preferably used in the present invention, andwill be referred to hereafter as the Visual Smoothness metric (VS).Other equations that also relate to the visual smoothness of the meshmay also be used.

The VS metric requires connectivity information such as the adjacentvertices of every vertex m_(t). For the case of the 3D-Animation codec,where it is assumed that no connectivity changes during the animation,the vertex adjacencies can be precomputed.

The BOP structure described above is suitable for the design of anefficient packetization scheme that employs redundancy information basedon RS erasure codes. The relation of its design parameters was alsoshown in Eq. 6. This equation, though, does not reflect any informationabout layering. An exemplary layering design approach is described next,followed by the proposed error resilient method for 3-D wireframestreaming.

The layering is performed in a way that the average VS value of eachlayer reflects its importance in the animation sequence. To achievethis, the VS from Eq. 8 is computed for every node in the meshindependently and the nodes are ordered according to their average VS inthe sequence. A node, or group of nodes, with the highest average VSforms the first and most important layer visually, L₀. This is the layerthat should be more resilient to packet errors than other layers.Subsequent importance layers L₁, . . . , L_(M) are created bycorrespondingly subsequent nodes, or group of nodes, in the VS order.

If a 3-D mesh has more nodes than the desirable number of layers, thenthe number of nodes to be grouped in the same layer is a design choice,and dictates the output bitrate of the layer. For meshes with only a fewnodes but large number of vertices per node, node partitioning might bedesirable. The partitioning would restructure the 3-D mesh's vertices ina new mesh with more nodes than originally. This process will affectconnectivity, but not the overall rendered model. Mesh partitioning intonodes, if it is possible, should not be arbitrary, but should ratherreflect the natural objects these new nodes will represent in the 3-Dscene and their corresponding motion. If partitioning is not possible inthe above sense, one could partition the mesh in arbitrary sizedsub-meshes (nodes) that will be allocated to the same layer. Meshpartitioning may require complex pre-processing steps that would beunderstood by one of skill in the art. Recall, however, that the3D-Animation codec assumes static connectivity.

It is common practice in “natural video” to build layers with acumulative effect. That is, layer L_(j) data add detail to the data oflayer L_(j-1) and improve the overall quality of video. But, one candecode only up to layer L_(j-1) and forget about the refinement layers.This approach may be taken in an adaptive streaming scenario, where asender may choose to send only j−1 layers during congested networkconditions, and j or more layers when the network conditions improve,i.e., more bandwidth becomes available.

The nature of 3-D animation layers disclosed herein is not alwayscumulative in the same sense. Decoding layer L_(j) (which has been builtwith appropriate node grouping or node partitioning) does notnecessarily only refine the quality of data contained in previous layersL₀ . . . L_(j-1), but adds animation details to the animated model by,for example, adding animation to more vertices in the model.

As an example, consider the sequence TELLY (discussed more fully below),which is a head-and-shoulders talking avatar. TELLY always faces thecamera (static camera). Since the camera does not move, it is a waste ofbandwidth to animate the back side of the hair. However, one can easilydetect the visible and invisible parts (set of vertices) of the hair andwith appropriate partitioning of node “hair” to allocate the visiblepart to layer L_(j-1) and the invisible part to layer L_(j). In the caseof a static camera (and where no interactivity is allowed) layer L_(j)is not transmitted. Thus when a user views the wireframe mesh oranimation in a static mode, only the visible portions of the animationcan be seen since the animation does not rotate or move. In the casewhere the user should be able to examine the animation by rotating orzooming in on the avatar (or other model), or look at the back side ofit, layer L_(j) is sent. In this case, the user views the animation inan interactive mode that enables the user to view portions of theanimation that were invisible in the static mode, due to the lack ofmotion of the animation. But, layer L_(j) does not refine the animationof the visible node of the hair in layer L_(j-1). It contains additionalanimation data for the invisible vertices. This provides an exampleresult of the partitioning method.

Further, the “interactive mode” does not necessarily require userinteraction with the animation. The interactive mode refers to anyviewing mode wherein the animation can move or rotate to expose aportion of the animation previously hidden. Thus, in some cases wherethe viewer is simply looking at the animation, the animation may moveand rotate in a more human or natural way while speaking. In this case,the L_(j) layer or other invisible layers may be sent to provide theadditional animation data to complete the viewing experience. In thisregard, the static or interactive mode may depend on bandwidthavailable. I.e., if enough bandwidth is available to transmit bothvisible and invisible layers of the animation, then the animation can beviewed in an interactive mode instead of a static mode. In anotheraspect of the invention, the user may select the static or interactivemode and thus control what layers are transmitted.

FIG. 3A illustrates an example set of steps according to an aspect ofthe invention. The method comprises partitioning the 3-D wireframe mesh(302), computing the VS value for each node in the mesh (304) andlayering data associated with the wireframe mesh into a plurality oflayers such that an average VS value associated with each layer reflectsthe respective layer's importance in an animation sequence (306). Thesame overall bitrate is maintained when transmitting the plurality oflayers by applying the error correction code to each layer where theerror correction code is unequal in the layer according to the layer'simportance (308).

The terms “partition” as used herein can mean a preprocessing step suchas partitioning the mesh into arbitrary or non-arbitrary sub-meshes thatwill be allocated to the same layer. Further, the term may also haveother applications, such as the process of generating the various layerscomprising one or more nodes.

FIG. 3B illustrates a flowchart of another aspect of the invention. Themethod comprises allocating more redundancy to a layer of the pluralityof layers that exhibits the greatest visual distortion (320). This maybe, for example, a layer comprising visually coarse information. Next,the redundancy is gradually reduced on layers having less contributionto visual smoothness (322). Interpolation-based concealment is appliedto each layer at the receiver where an irrecoverable loss of packetsoccurs only within the respective layer (324) from the standpoint of thereceiver. As packets belonging to a particular layer travel through thecommunications network, they may take different paths from the sender tothe receiver, thus suffering variable delays and losses. When thereceiver sees an overall packet loss rate, the receiver will try toreduce the loss rate by using the redundant information (FEC) providedseparately in each layer. The amount of FEC may not be enough to recoverall missing packets (residual packets). The interpolation-basedconcealment can be applied to each layer independently to reduce thedistortion introduced by residual packet loss. In general, steps 320 and322 are performed on the coding/transmitter end and step 324 isperformed at the receiver over a communications network, such as apeer-to-peer network.

The expected distortion of the animation at the receiver at time t isthe sum of the product quantities P_(jt)·D_(jt), where j is the layerindex, D_(jt) is the visual distortion incurred by missing informationin layer j at time t, and P_(jt) is the probability of having anirrecoverable packet loss in layer j. By the way we constructed thelayers, the probabilities P_(jt) are independent, and a burst packetloss in a layer contributes its own visual distortion D_(jt) in thedecoded sequence. Formally, the expected visual smoothness VS_((t)) ofan animation at the decoder at time t can be expressed as:

$\begin{matrix}{{{VS}_{(t)} = {\sum\limits_{j = 0}^{L - 1}{P_{jt}D_{jt}}}},{\text{∀}t}} & (9)\end{matrix}$

where L is the number of layers. In the equation above, P_(jt) is theblock error rate P_(BER) as given by Eq. 5, or the probability of losingmore than n−k_(j) packets in layer j. Using the block error densityfunction P (m, n), the following is derived:

$\begin{matrix}{P_{jt} = {\sum\limits_{m = {n - k_{jt} + 1}}^{n}{P\left( {m,n} \right)}}} & (10)\end{matrix}$

From Eqs. 9 and 10, VS_((t)) can be described as:

$\begin{matrix}{{{VS}_{(t)} = {\sum\limits_{j = 0}^{L - 1}{\sum\limits_{m = {n - k_{jt} + 1}}^{n}{{P\left( {m,n} \right)}D_{jt}}}}},{\text{∀}t}} & (11)\end{matrix}$

Equation 11 estimates in a statistical sense the expected visualsmoothness experienced per frame at the decoder. The objective is tominimize this distortion with respect to the values of k_(jt)'s in Eq.11. From the way the bitstream is split into layers it is expected thatthe optimization process allocates more redundancy to the layer thatexhibits the greatest visual distortion (coarse layer), and graduallyreduces the redundancy rate on layers with finest contribution to theoverall smoothness. There are L values of k_(jt) that need to becalculated at every time t, that follow the conditions 0≦k_(jt)≦n andΣ_(j=0) ^(L-1)(n−k_(jt))=R_(C)/q where R_(C) the redundancy bits, and qis the symbol size. The above problem formulation yields a non-linearconstraint optimization problem that can be solved numerically.

The anticipated behavior of the model for P_(B)=0 is to produce equalvalues for k_(jt)'s, whereas in high P_(B)'s unequally varying k_(jt)'swould be obtained. Note that for the calculation of smoothnessdistortions in Eq. 11, it is assumed that no error concealment takesplace at the receiver.

It has been shown that techniques based on vertex linear interpolationare a sufficient and efficient method of error concealment for3D-Animation frames. This relies on the ‘locality of referenceprinciple’, according to which high-frame rate animations are unlikelyto exhibit vertex trajectories other than linear or piece-wise linear.If higher complexity can be accommodated, higher order interpolation canbe employed by using information from the neighboring frames. Some knowninterpolation and other concealment methods are generic in that they canbe used by any other decoder.

FIG. 4 is a graph 400 illustrating the relative performances of threeerror concealment methods adapted to the experimental parameters of thiswork, namely P_(B)=[0 . . . 30] and L_(B)=4. It is evident that linearinterpolation outperforms Frame Repetition or Motion Vector-basedmethods. The plot shows average values for 8 iterations with differentloss patterns. It is clear on the plot (as seen by the error bars) thatthe interpolation concealment method exhibits very low variance,verifying the locality of reference principle (the average loss burstlength L_(B)=4 is much lower than the sequence frame rate of 30 Hz.)Therefore, the present invention preferably uses interpolation-basederror concealment at the receiver in the case where the channel decoderreceives less than n−k_(jt) BOP packets. In fact, the k_(jt)'s thatprovide a solution to the optimization problem, will also give minimumdistortion if combined with concealment techniques. The expecteddistortion in such cases will be lower than the distortion without errorconcealment.

The following explains the experimental procedure and how to tune thevalues and the optimization process for a real-world case of 3-Dwireframe animation, along with discussion of experimental results usingthe present invention.

The following experiments demonstrate through simulation the efficiencyof the proposed Unequal Error Protection (UEP) scheme combined withError Concealment (EC) for streaming 3-D wireframe animations. Inparticular, using UEP with EC is compared to simple UEP, to Equal ErrorProtection (EEP) and to No Protection (NP). The comparison is based onthe Visual Smoothness metric, which is known to yield a distortionmeasure that captures the surface smoothness of the time-dependent meshduring the animation. For the calculation of the parameters k_(jt), theconstrained minimization problem of Eq. 11 is numerically solved, giventhe channel rate R_(C). Furthermore, n is calculated from Eq. 6 suchthat the rate characteristics of the original source signal are met forthe particular design of a BOP. The other parameters used in Eq. 6 aregiven below for the two sequences in the experiments, and are alsosummarized in Table 1.

TABLE 1 ANIMATION SEQUENCE PARAMETERS USED IN THE REDUNDANCYEXPERIMENTS: TELLY & BOUNCEBALL. Sequence TELLY df_(TELLY) 0.75 Nodes 9Frame Rate 30 Hz Source Rate 220 Kbps Channel Rate 33 Kbps Frames 780Layer 0 UpperLip LowerLip Tongue Layer 1 Skin Teeth Layer 2 EyeLashEyeBrow EyeCorner Nostril Sequence BOUNCEBALL df_(BBALL) 1.0 Nodes 1Frame Rate 24 Hz Source Rate 61 Kbps Channel Rate 9.15 Kbps Frames 528Layer 0 Bounceball TELLY BBALL L₀: S_(P) 264 200 P_(BOP) 16 35 L₁: S_(P)264 200 P_(BOP) 19 35 L₂: S_(P) 150 N/A P_(BOP) 50 N/A

For the EEP case, a constant k is considered that can be deriveddirectly from the selection of the channel rate, which is set to 15%.For the NP case, all available channel rates to the source areallocated. Finally, an EC scheme was used based on interpolation for thecase of UEP with residual losses. In all experiments used L_(B)=4.

The sequences TELLY and BOUNCEBALL were used with density factors ofdf_(TELLY)=0.75 and df_(BBALL)=1.0 given by Eq. 2. TELLY consists of 9nodes (out of which 3 are relatively sparse, and the remaining 6 arecomplete) and totals 780 frames at 30 Hz as shown in Table I. Itsaverage source bitrate is R_(S,TELLY)=220 Kbps. BOUNCEBALL only has 1complete node and 528 frames at 24 Hz, forming 1 layer of source rateR_(S,BALL)=61 Kbps average. Both sequences have been coded with I-framesat every 15 frames. Roughly 15% of channel coding redundancy wasallowed, resulting in total source and channel coding redundancy,resulting in total source and channel rates of R_(TELLY)=253 Kbps andR_(BBALL)=70.15 Kbps. Choosing n=32 the parameters, from Eq. 6 thecalculations for each layer's packetization are tabulated in Table I.The value of n is chosen as a compromise between latency and efficiency,since higher n makes the RS codes more resilient, by sacrificing delayand buffer space.

Sequence TELLY was split into 3 layers according to the suggestedlayering method presented in Section V, each consisting of the nodesshown in Table I. Each layer's fraction of the total number of animatedvertices in the 3-D mesh is (L₀, L₁, L₂)=(0.48, 0.42, 0.10) on average.This splitting is expected to reflect the source bitrates of each layerproportionally. It was noticed that the suggested layering schemeallocated 2 out of 3 sparse nodes to the same layer, L₁. The totalnumber of vertices of these two sparse nodes represents 65% of thevertices in the reference mesh. The third sparse node, Nostril, wasallocated to layer L₂, but its individual motion relates to a very smallfraction of the model's total number of vertices (≈1.3%). This fact maybear some significance if one desires to relate the node-to-layerallocation (using the VS metric) to the density factor df_(L),calculated per layer⁴ (Eq. 2), and to the output bitrates. If suchrelation exists, a dynamic layering scheme may be developed forapplications with such needs.

Sequence BOUNCEBALL initially contains only one node. The sequencerepresents a soft ball with inherent symmetry around a center point asits shape implies. The ball also deforms slightly as it bounces. Giventhe shape symmetry, it was decided to partition the mesh into 2 nodes ofequal number of vertices without respect to the VS metric for each node.The logic behind this partitioning is to attempt to verify the effectthe VS metric has on the proposed UEP resilience scheme. All othersource coding parameters are constant between the two layers, mostimportantly the quantization step size. It is anticipated that bothlayers will receive roughly equal average protection bits, so that UEPperformance will approach that of EEP.

FIG. 5A depicts a first diagram 502 illustrating VS as a function of theaverage packet loss rate, P_(B), for TELLY. The four curves on the plotrepresent each suggested resilience method, for the code (31, 22). Theaverage calculated codes for the UEP are as follows (rounded to nearestinteger): (n, k ₀)=(31,19), (n, k ₁)=(31,23), (n, k ₂)=(31,28). It isclear that UEP, and UEP+EC outperform NP and EEP for medium to high lossrates of P_(B)>9%. Recall that the layering is performed in such a waythat the lowest layer exhibited high average visual distortion. Sincethe UEP method allocates higher codes to the lower layer (L₀), betterresilience is expected for L₀ at high loss rates. This factor dominatesin the average distortion, resulting in better performance. At low lossrates it was noticed that EEP and UEP behave in approximately the sameway, as the RS codes are more than sufficient to recover all or mosterrors. It is also noted that the NP method under conditions of no lossis much better than any other. This is an intuitive result, since sourceinformation takes all available channel rate, thus better encoding thesignal. It is also worth noticing the effect of EC: the distortion ofthe UEP+EC scheme is slightly improved over the simple UEP case. This isalso expected.

The results for the (31,27) RS code on sequence TELLY, shown in plot 504of FIG. 5B, are similar. Here, the threshold where the UEP methods (withor without EC) take over EEP or NP is around P_(B)=7%. Note how theinitial NP performance (low P_(B)'s) is steep compared to the (31, 22),highlighting again the fact that channel coding bits are actually‘wasted’ since they do not contribute much resilience in this low lossregion, at the expense of source rate. The corresponding average codesper layer are: (n, k ₀)=(31,26), (n, k ₁)=(31,28), (n, k ₂)=(31,30).There is an improvement again in the UEP method's performance resultingfrom the error concealment's interpolation algorithm. As this quantityhas not been accounted for in the optimization problem it is expected tocontribute a small reduction to the visual error.

FIG. 6 shows the results 602 achieved for the same experiment repeatedover the BOUNCEBALL sequence, which was ‘symmetrically’ layered asdescribed earlier in this section. The same (31, 22) EEP code was usedas before for comparison. The graph 602 shows the same trends andrelative performances as in TELLY, with UEP+EC being the one giving thebest overall performance. It is noted, however, that the distance of theUEP curves from the EEP ones decreased considerably compared to theTELLY sequence at high P_(B)'s. The average integer calculated RS codesfor the UEP case are: (n, k ₀)=(31,22), (n, k ₁)=(31,22), i.e.equivalent to the EEP case. This may be a surprising result at the firstglance, but careful reasoning suggests that equally balanced layers interms of the amount of animation they contain (same number of vertices,nodes, very similar motion in the scene, and same encoding parameters)correspond to visually balanced distortions. This is exactly theexpected result when layering for the BOUNCEBALL sequence describedabove. In fact, the real values of k_(0t), k_(1t) computed as thesolution to the optimization problem, vary around the average integervalue of 22. Furthermore, recall that the original symmetric BOUNCEBALLmesh was partitioned into two arbitrary nodes without consideration totheir individual visual distortions, which were assumed to be similar.In fact, the softball's deformation at the bouncing points reduces thesymmetry of the original shape. These facts reasonably explain why theUEP and EEP curves are not accurately fit at higher P_(B)'s as one wouldnormally expect. Finally, it is noted that the UEP+EC method provides aslight, but hardly noticeable, improvement to the visual distortion asin the previous experiment.

The present invention addresses the fundamental problem of how best toutilize the available channel capacity for streaming 3-D wireframeanimation in such a way as to achieve optimal subjective resilience toerror. In short, the invention links channel coding, packetization, andlayering with a subjective parameter that measures visual smoothness inthe reconstructed image. On this basis, it is believed that the resultmay help open the way for 3-D animation to become a serious networkedmedia type. The disclosed methods attempt to optimize the distributionof the bit budget allocation reserved for channel coding amongstdifferent layers, using a metric that reflects the human eye's visualproperty of detecting surface smoothness on time-dependent meshes. Usingthis metric, the encoded bitstream is initially partitioned into layersof visual importance, and experimental results show that UEP combinedwith EC yields good protection against burst packet errors occurring onthe Internet.

Embodiments within the scope of the present invention may also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or combination thereof) to a computer, the computerproperly views the connection, either wired or wireless, as acomputer-readable medium. Thus, any such connection is properly termed acomputer-readable medium. Combinations of the above should also beincluded within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,objects, components, and data structures, etc. that perform particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other embodiments of theinvention may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. For example, peer-to-peer distributedenvironments provide an ideal communications network wherein theprinciples of the present invention would apply and be beneficial. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

Although the above description may contain specific details, they shouldnot be construed as limiting the claims in any way. Other configurationsof the described embodiments of the invention are part of the scope ofthis invention. Accordingly, the appended claims and their legalequivalents should only define the invention, rather than any specificexamples given.

We claim:
 1. A method comprising: processing, via a processor,parameters n, FR, Sp, and of video data via a channel bitrate algorithm,wherein the parameters are located in a block-of-packets structure foreach layer of a plurality of layers of video data, wherein R is abitrate, n is a number of lines in the block-of-packets structure, Sp isa number of columns in the block-of-packets structure, FR is a sequenceframe rate, and F is a number of data frames in the block-of-packetsstructure for a respective layer of the plurality of layers; andapplying, via the processor, unequal error protection to each respectivelayer of the plurality of layers according to a result of the channelbitrate algorithm.
 2. The method of claim 1, wherein each respectivelayer of the plurality of layers comprises one of a node and a group ofnodes within a wireframe mesh.
 3. The method of claim 1, furthercomprising encoding a particular layer in the plurality of layers to beresilient to packet errors.
 4. The method of claim 1, wherein a numberof nodes within a portion of the plurality of layers is associated withan output bit rate of the portion.
 5. The method of claim 1, furthercomprising producing a three-dimensional packetized streaming signalrepresentative of a scene comprising animation associated with theplurality of layers of video data.
 6. The method of claim 1, furthercomprising partitioning the plurality of layers according to a visualimportance of each respective layer in the plurality of layers.
 7. Themethod of claim 1, wherein the unequal error protection scheme comprisesoptimizing a distribution of a bit budget allocation amongst theplurality of layers.
 8. A system comprising: a processor; and acomputer-readable storage medium having instructions stored which, whenexecuted by the processor, cause the processor to perform operationscomprising: processing parameters n, FR, Sp, and F of video data via achannel bitrate algorithm, wherein the parameters are located in ablock-of-packets structure for each layer of a plurality of layers ofvideo data, wherein R is a bitrate, n is a number of lines in theblock-of-packets structure, Sp is a number of columns in theblock-of-packets structure, FR is a sequence frame rate, and F is anumber of data frames in the block-of-packets structure for a respectivelayer of the plurality of layers; and applying unequal error protectionto each respective layer of the plurality of layers according to aresult of the channel bitrate algorithm.
 9. The system of claim 8,wherein each respective layer of the plurality of layers comprises oneof a node and a group of nodes within a wireframe mesh.
 10. The systemof claim 8, the computer-readable storage medium having additionalinstructions stored which, when executed by the processor, result inoperations comprising encoding a particular layer in the plurality oflayers to be resilient to packet errors.
 11. The system of claim 8,wherein a number of nodes within a portion of the plurality of layers isassociated with an output bit rate of the portion.
 12. The system ofclaim 8, the computer-readable storage medium having additionalinstructions stored which, when executed by the processor, result inoperations comprising producing a three-dimensional packetized streamingsignal representative of a scene comprising animation associated withthe plurality of layers of video data.
 13. The system of claim 8, thecomputer-readable storage medium having additional instructions storedwhich, when executed by the processor, result in operations comprisingpartitioning the plurality of layers according to a visual importance ofeach respective layer in the plurality of layers.
 14. The system ofclaim 8, wherein the unequal error protection scheme comprisesoptimizing a distribution of a bit budget allocation amongst theplurality of layers.
 15. A computer-readable storage device havinginstructions stored which, when executed by the computing device, causethe computing device to perform operations comprising: processingparameters n, FR, Sp, and F of video data via a channel bitratealgorithm, wherein the parameters are located in a block-of-packetsstructure for each layer of a plurality of layers of video data, whereinR is a bitrate, n is a number of lines in the block-of-packetsstructure, Sp is a number of columns in the block-of-packets structure,FR is a sequence frame rate, and F is a number of data frames in theblock-of-packets structure for a respective layer of the plurality oflayers; and applying unequal error protection to each respective layerof the plurality of layers according to a result of the channel bitratealgorithm.
 16. The computer-readable storage device of claim 15, whereineach respective layer of the plurality of layers comprises one of a nodeand a group of nodes within a wireframe mesh.
 17. The computer-readablestorage device of claim 15, having additional instructions stored which,when executed by the computing device, result in operations comprisingencoding a particular layer in the plurality of layers to be resilientto packet errors.
 18. The computer-readable storage device of claim 15,wherein a number of nodes within a portion of the plurality of layers isassociated with an output bit rate of the portion.
 19. Thecomputer-readable storage device of claim 15, having additionalinstructions stored which, when executed by the computing device, resultin operations comprising producing a three-dimensional packetizedstreaming signal representative of a scene comprising animationassociated with the plurality of layers of video data.
 20. Thecomputer-readable storage device of claim 15, having additionalinstructions stored which, when executed by the computing device, resultin operations comprising partitioning the plurality of layers accordingto a visual importance of each respective layer in the plurality oflayers.